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Double Penalized Semi-Parametric Signed-Rank Regression with Adaptive LASSO

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Abstract

In this paper, a semi-parametric regression model with an adaptive LASSO penalty imposed on both the linear and the nonlinear components of the mode is considered. The model is rewritten so that a signed-rank technique can be used for estimation. The nonlinear part consists of a covariate that enters the model nonlinearly via an unknown function that is estimated using B-splines. The author shows that the resulting estimator is consistent under heavy-tailed distributions and asymptotic normality results are given. Monte Carlo simulations as well as practical applications are studied to assess the validity of the proposed estimation method.

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Authors and Affiliations

  1. Trinity Place, Trinity University, San Antonio, TX, 78258, USA

    Eddy Kwessi

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  1. Eddy Kwessi
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Correspondence to Eddy Kwessi.

Additional information

This paper was recommended for publication by Editor DONG Yuexiao.

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Kwessi, E. Double Penalized Semi-Parametric Signed-Rank Regression with Adaptive LASSO. J Syst Sci Complex 34, 381–401 (2021). https://doi.org/10.1007/s11424-020-9097-9

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  • DOI: https://doi.org/10.1007/s11424-020-9097-9

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